Low level segmentation based image features are used for the problem of object categorization. In general, object categorization comprises two main research areas: (1) classification or clustering of images containing objects belonging to an object category, and (2) detection, localization, and segmentation of individual object-category instances in images. The first thrust of research is typically concerned with exemplar based methods, where the main focus is to develop an efficient distance measure between two images. Work in the second research area is primarily concerned with object-category modeling on training images, and using category models for object detection, localization and segmentation in test images. These approaches differ from object recognition methods in that category instances in the training and test sets are different.
- S. Todorovic and N. Ahuja, Learning Subcategory Relevances for Category Recognition, IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, June 2008.
- N. Ahuja and S. Todorovic, Learning the Taxonomy and Models of Categories Present in Arbitrary Images, Proc. IEEE International Conference on Computer Vision, Rio De Janeiro, Brazil, October 2007.